{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING: New definition \n",
      "    +(AbstractArray{T<:Any, 2}, WoodburyMatrices.SymWoodbury) at /Users/tamasnagy/.julia/v0.4/WoodburyMatrices/src/SymWoodburyMatrices.jl:106\n",
      "is ambiguous with: \n",
      "    +(DataArrays.DataArray, AbstractArray) at /Users/tamasnagy/.julia/v0.4/DataArrays/src/operators.jl:276.\n",
      "To fix, define \n",
      "    +(DataArrays.DataArray{T<:Any, 2}, WoodburyMatrices.SymWoodbury)\n",
      "before the new definition.\n",
      "WARNING: New definition \n",
      "    +(AbstractArray{T<:Any, 2}, WoodburyMatrices.SymWoodbury) at /Users/tamasnagy/.julia/v0.4/WoodburyMatrices/src/SymWoodburyMatrices.jl:106\n",
      "is ambiguous with: \n",
      "    +(DataArrays.AbstractDataArray, AbstractArray) at /Users/tamasnagy/.julia/v0.4/DataArrays/src/operators.jl:300.\n",
      "To fix, define \n",
      "    +(DataArrays.AbstractDataArray{T<:Any, 2}, WoodburyMatrices.SymWoodbury)\n",
      "before the new definition.\n"
     ]
    }
   ],
   "source": [
    "using DataFrames\n",
    "using Gadfly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><tr><th></th><th>technique</th><th>score</th><th>num_genes</th><th>coverage</th><th>representation</th><th>moi</th><th>σ</th><th>bin_info</th><th>bottleneck_representation</th><th>seq_depth</th><th>run_idx</th><th>crisprtype</th></tr><tr><th>1</th><td>snr</td><td>1.7437091225551875</td><td>500</td><td>5</td><td>10</td><td>0.25</td><td>1.0</td><td>0.5</td><td>10</td><td>10</td><td>1</td><td>CRISPRi</td></tr><tr><th>2</th><td>snr</td><td>1.8865564086006428</td><td>500</td><td>5</td><td>10</td><td>0.25</td><td>1.0</td><td>0.5</td><td>10</td><td>10</td><td>2</td><td>CRISPRi</td></tr><tr><th>3</th><td>snr</td><td>2.2687043808713527</td><td>500</td><td>5</td><td>10</td><td>0.25</td><td>1.0</td><td>0.5</td><td>10</td><td>10</td><td>3</td><td>CRISPRi</td></tr><tr><th>4</th><td>snr</td><td>1.6754887740932651</td><td>500</td><td>5</td><td>10</td><td>0.25</td><td>1.0</td><td>0.5</td><td>10</td><td>10</td><td>4</td><td>CRISPRi</td></tr><tr><th>5</th><td>snr</td><td>2.237104952860403</td><td>500</td><td>5</td><td>10</td><td>0.25</td><td>1.0</td><td>0.5</td><td>10</td><td>10</td><td>5</td><td>CRISPRi</td></tr><tr><th>6</th><td>snr</td><td>2.9668067836514336</td><td>500</td><td>5</td><td>10</td><td>0.25</td><td>1.0</td><td>0.5</td><td>10</td><td>10</td><td>6</td><td>CRISPRi</td></tr></table>"
      ],
      "text/plain": [
       "6×12 DataFrames.DataFrame\n",
       "│ Row │ technique │ score   │ num_genes │ coverage │ representation │ moi  │\n",
       "├─────┼───────────┼─────────┼───────────┼──────────┼────────────────┼──────┤\n",
       "│ 1   │ \"snr\"     │ 1.74371 │ 500       │ 5        │ 10             │ 0.25 │\n",
       "│ 2   │ \"snr\"     │ 1.88656 │ 500       │ 5        │ 10             │ 0.25 │\n",
       "│ 3   │ \"snr\"     │ 2.2687  │ 500       │ 5        │ 10             │ 0.25 │\n",
       "│ 4   │ \"snr\"     │ 1.67549 │ 500       │ 5        │ 10             │ 0.25 │\n",
       "│ 5   │ \"snr\"     │ 2.2371  │ 500       │ 5        │ 10             │ 0.25 │\n",
       "│ 6   │ \"snr\"     │ 2.96681 │ 500       │ 5        │ 10             │ 0.25 │\n",
       "\n",
       "│ Row │ σ   │ bin_info │ bottleneck_representation │ seq_depth │ run_idx │\n",
       "├─────┼─────┼──────────┼───────────────────────────┼───────────┼─────────┤\n",
       "│ 1   │ 1.0 │ 0.5      │ 10                        │ 10        │ 1       │\n",
       "│ 2   │ 1.0 │ 0.5      │ 10                        │ 10        │ 2       │\n",
       "│ 3   │ 1.0 │ 0.5      │ 10                        │ 10        │ 3       │\n",
       "│ 4   │ 1.0 │ 0.5      │ 10                        │ 10        │ 4       │\n",
       "│ 5   │ 1.0 │ 0.5      │ 10                        │ 10        │ 5       │\n",
       "│ 6   │ 1.0 │ 0.5      │ 10                        │ 10        │ 6       │\n",
       "\n",
       "│ Row │ crisprtype │\n",
       "├─────┼────────────┤\n",
       "│ 1   │ \"CRISPRi\"  │\n",
       "│ 2   │ \"CRISPRi\"  │\n",
       "│ 3   │ \"CRISPRi\"  │\n",
       "│ 4   │ \"CRISPRi\"  │\n",
       "│ 5   │ \"CRISPRi\"  │\n",
       "│ 6   │ \"CRISPRi\"  │"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = readtable(\"data/bigiron_facs_binning_snr_d56c7a8_clean.csv\")\n",
    "# df[df[:technique] .== \"signal\", :score] = log2(df[df[:technique] .== \"signal\", :score])\n",
    "# deleterows!(df, find(df[:technique] .== \"snr\"))\n",
    "head(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><tr><th></th><th>technique</th><th>score</th><th>num_genes</th><th>coverage</th><th>moi</th><th>bin_info</th><th>run_idx</th><th>crisprtype</th><th>rep_lvl</th></tr><tr><th>1</th><td>noise</td><td>0.08648508612915688</td><td>500</td><td>5</td><td>0.25</td><td>0.5</td><td>1</td><td>CRISPRKO</td><td>high</td></tr><tr><th>2</th><td>noise</td><td>0.08207099495593766</td><td>500</td><td>5</td><td>0.25</td><td>0.5</td><td>2</td><td>CRISPRKO</td><td>high</td></tr><tr><th>3</th><td>noise</td><td>0.09211835724887645</td><td>500</td><td>5</td><td>0.25</td><td>0.5</td><td>3</td><td>CRISPRKO</td><td>high</td></tr><tr><th>4</th><td>noise</td><td>0.09430858250583832</td><td>500</td><td>5</td><td>0.25</td><td>0.5</td><td>4</td><td>CRISPRKO</td><td>high</td></tr><tr><th>5</th><td>noise</td><td>0.07918669012050547</td><td>500</td><td>5</td><td>0.25</td><td>0.5</td><td>5</td><td>CRISPRKO</td><td>high</td></tr><tr><th>6</th><td>noise</td><td>0.09005507195498429</td><td>500</td><td>5</td><td>0.25</td><td>0.5</td><td>6</td><td>CRISPRKO</td><td>high</td></tr></table>"
      ],
      "text/plain": [
       "6×9 DataFrames.DataFrame\n",
       "│ Row │ technique │ score     │ num_genes │ coverage │ moi  │ bin_info │\n",
       "├─────┼───────────┼───────────┼───────────┼──────────┼──────┼──────────┤\n",
       "│ 1   │ \"noise\"   │ 0.0864851 │ 500       │ 5        │ 0.25 │ 0.5      │\n",
       "│ 2   │ \"noise\"   │ 0.082071  │ 500       │ 5        │ 0.25 │ 0.5      │\n",
       "│ 3   │ \"noise\"   │ 0.0921184 │ 500       │ 5        │ 0.25 │ 0.5      │\n",
       "│ 4   │ \"noise\"   │ 0.0943086 │ 500       │ 5        │ 0.25 │ 0.5      │\n",
       "│ 5   │ \"noise\"   │ 0.0791867 │ 500       │ 5        │ 0.25 │ 0.5      │\n",
       "│ 6   │ \"noise\"   │ 0.0900551 │ 500       │ 5        │ 0.25 │ 0.5      │\n",
       "\n",
       "│ Row │ run_idx │ crisprtype │ rep_lvl │\n",
       "├─────┼─────────┼────────────┼─────────┤\n",
       "│ 1   │ 1       │ \"CRISPRKO\" │ high    │\n",
       "│ 2   │ 2       │ \"CRISPRKO\" │ high    │\n",
       "│ 3   │ 3       │ \"CRISPRKO\" │ high    │\n",
       "│ 4   │ 4       │ \"CRISPRKO\" │ high    │\n",
       "│ 5   │ 5       │ \"CRISPRKO\" │ high    │\n",
       "│ 6   │ 6       │ \"CRISPRKO\" │ high    │"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[!isfinite(df[:score]), :score] = 0.0\n",
    "df[:rep_lvl] = :none\n",
    "for (rep_amt, noise_lvl) in zip([(10, 10, 10, 1.0), (100, 100, 100, 1.0), (1000, 1000, 1000, 0.5)], [:low, :med, :high])\n",
    "    selection = [\n",
    "        (:representation, rep_amt[1]),\n",
    "        (:bottleneck_representation, rep_amt[2]), \n",
    "        (:seq_depth, rep_amt[3]), \n",
    "        (:σ, rep_amt[4])\n",
    "    ]\n",
    "    df[vec(all(hcat([(df[item[1]] .== item[2]) for item in selection]...), 2)), :rep_lvl] = noise_lvl\n",
    "end\n",
    "delete!(df, [:representation, :seq_depth, :bottleneck_representation, :σ])\n",
    "subdfs = []\n",
    "for subdf in groupby(df, [:technique, :rep_lvl, :crisprtype])\n",
    "    subdf[:score] = subdf[:score] ./ maximum(subdf[:score])\n",
    "    push!(subdfs, subdf)\n",
    "end\n",
    "df = vcat(subdfs...)\n",
    "head(df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table class=\"data-frame\"><tr><th></th><th>technique</th><th>rep_lvl</th><th>bin_info</th><th>crisprtype</th><th>std_score</th><th>mean_score</th><th>score_max</th><th>score_min</th><th>n</th></tr><tr><th>1</th><td>noise</td><td>high</td><td>0.025</td><td>CRISPRKO</td><td>0.11887850887142379</td><td>0.47103119651833225</td><td>0.5323725070959869</td><td>0.40968988594067757</td><td>25</td></tr><tr><th>2</th><td>noise</td><td>high</td><td>0.025</td><td>CRISPRi</td><td>0.13924719730605942</td><td>0.5418465194690395</td><td>0.6136980732789662</td><td>0.4699949656591128</td><td>25</td></tr><tr><th>3</th><td>noise</td><td>high</td><td>0.041379310344827586</td><td>CRISPRKO</td><td>0.0302168423140523</td><td>0.3129053783429664</td><td>0.32849726897701736</td><td>0.2973134877089154</td><td>25</td></tr><tr><th>4</th><td>noise</td><td>high</td><td>0.041379310344827586</td><td>CRISPRi</td><td>0.08960722137459044</td><td>0.3196067056665062</td><td>0.36584403189579484</td><td>0.2733693794372175</td><td>25</td></tr><tr><th>5</th><td>noise</td><td>high</td><td>0.05775862068965517</td><td>CRISPRKO</td><td>0.01821433355619863</td><td>0.25889667804416694</td><td>0.26829527415916543</td><td>0.24949808192916845</td><td>25</td></tr><tr><th>6</th><td>noise</td><td>high</td><td>0.05775862068965517</td><td>CRISPRi</td><td>0.0347040534411537</td><td>0.25601343397369</td><td>0.2739207255493253</td><td>0.23810614239805467</td><td>25</td></tr></table>"
      ],
      "text/plain": [
       "6×9 DataFrames.DataFrame\n",
       "│ Row │ technique │ rep_lvl │ bin_info  │ crisprtype │ std_score │ mean_score │\n",
       "├─────┼───────────┼─────────┼───────────┼────────────┼───────────┼────────────┤\n",
       "│ 1   │ \"noise\"   │ high    │ 0.025     │ \"CRISPRKO\" │ 0.118879  │ 0.471031   │\n",
       "│ 2   │ \"noise\"   │ high    │ 0.025     │ \"CRISPRi\"  │ 0.139247  │ 0.541847   │\n",
       "│ 3   │ \"noise\"   │ high    │ 0.0413793 │ \"CRISPRKO\" │ 0.0302168 │ 0.312905   │\n",
       "│ 4   │ \"noise\"   │ high    │ 0.0413793 │ \"CRISPRi\"  │ 0.0896072 │ 0.319607   │\n",
       "│ 5   │ \"noise\"   │ high    │ 0.0577586 │ \"CRISPRKO\" │ 0.0182143 │ 0.258897   │\n",
       "│ 6   │ \"noise\"   │ high    │ 0.0577586 │ \"CRISPRi\"  │ 0.0347041 │ 0.256013   │\n",
       "\n",
       "│ Row │ score_max │ score_min │ n  │\n",
       "├─────┼───────────┼───────────┼────┤\n",
       "│ 1   │ 0.532373  │ 0.40969   │ 25 │\n",
       "│ 2   │ 0.613698  │ 0.469995  │ 25 │\n",
       "│ 3   │ 0.328497  │ 0.297313  │ 25 │\n",
       "│ 4   │ 0.365844  │ 0.273369  │ 25 │\n",
       "│ 5   │ 0.268295  │ 0.249498  │ 25 │\n",
       "│ 6   │ 0.273921  │ 0.238106  │ 25 │"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_info = by(df, [:technique, :rep_lvl, :bin_info, :crisprtype]) do grouped_df\n",
    "    grouped_df = grouped_df[1:25, :]\n",
    "    n = size(grouped_df, 1)\n",
    "    mean_score = mean(grouped_df[:score])\n",
    "    std_score = std(grouped_df[:score])\n",
    "    conf_int = 2.58 * std_score./sqrt(n)\n",
    "    DataFrame(\n",
    "        std_score = std_score,\n",
    "        mean_score = mean_score,\n",
    "        score_max = mean_score + conf_int,\n",
    "        score_min = mean_score - conf_int,\n",
    "        n = n\n",
    "    )\n",
    "end\n",
    "deleterows!(grouped_info, collect(1:size(grouped_info, 1))[grouped_info[:rep_lvl] .== :low])\n",
    "head(grouped_info)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
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   ],
   "source": [
    "lmhlt(x, y) = x == :low && y != :low || x == :med && y == :high\n",
    "sort!(grouped_info, lt=lmhlt)\n",
    "\n",
    "p = plot(grouped_info, x=:bin_info, y=:mean_score, ymax=:score_max, ymin=:score_min, \n",
    "color=:technique, xgroup=:crisprtype, ygroup=:rep_lvl,\n",
    "Geom.subplot_grid(Geom.line, Geom.ribbon, Coord.cartesian(xmax=-1)), Scale.x_log2,\n",
    "Guide.title(\"FACS screens need to balance signal vs noise (n=25) 99% conf interval\"))\n",
    "\n",
    "draw(SVG(\"plots/signal_noise_tradeoff.svg\", 15cm, 12cm), p)\n",
    "p.theme = Theme(background_color=colorant\"white\")\n",
    "draw(SVG(15cm, 12cm), p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": []
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